How does concept extract from medical documents?
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Concept extraction from medical documents involves the process of automatically identifying and extracting clinical concepts such as problems, diagnosis, and treatment from unstructured clinical narrative documents. Various approaches have been used for concept extraction, including rule-based mechanisms, machine learning, and deep learning. Rule-based systems, such as the one proposed by Abbas et al. , use linguistic and lexical semantic features to extract clinical concepts with higher accuracy and transparency. Machine learning approaches, like the one developed by Kraljevic et al. , use unsupervised learning to disambiguate entities and improve the performance of named entity recognition and linking (NER+L). These approaches leverage tools like cTAKES, Sophia, MetaMap, and the Unified Medical Language System (UMLS) Metathesaurus to extract and classify concepts from medical documents. The extracted concepts can then be used for data-driven applications, such as clinical decision-support systems and disease progression analysis.
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Open access•Posted Content | The paper describes the use of the Medical Concept Annotation Tool (MedCAT) to extract and structure information from biomedical documents, such as Electronic Health Records (EHRs). MedCAT uses unsupervised machine learning to disambiguate entities and has been validated on MIMIC-III and MedMentions datasets. |
17 Mar 2023 1 Citations | The paper uses a Stanford parser to convert medical documents into sentences and then into noun phrases and verb phrases, which are considered as concepts for ontology mapping. |
Open access 28 Jul 2019 5 Citations | The paper proposes a generic algorithm that uses the Unified Medical Language System (UMLS) to extract medical concepts and entities from clinical unstructured documents. |
17 Mar 2023 | The paper proposes a concept-based ontology mapping method to extract concepts from medical documents using a Stanford parser and Parts-of-speech tagger. |
2 Citations | The paper proposes a rule-based system that automatically extracts clinical concepts from unstructured medical documents using linguistic and lexical semantic features. |
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